Medical assistance in dying: A political issue for nurses and nursing in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Death and dying are natural phenomena embedded within complex political, cultural and social systems. Nurses often practice at the forefront of this process and have a fundamental role in caring for both patients and those close to them during the process of dying and following death. While nursing has a rich tradition in advancing the palliative and end-of-life care movement, new modes of care for patients with serious and irremediable medical conditions arise when assisted death is legalized in a particular jurisdiction. In early 2015, the Supreme Court of Canada released its landmark decision Carter v. Canada (Attorney General) ('Carter'), which legalized physician-assisted suicide in particular clinical situations. The new law provided the broad national framework for Medical Assistance in Dying (MAiD) in Canada but, once the law was passed, provincial and territorial governments and health professional regulatory bodies each had to undertake a process of developing policies, procedures and processes to guide MAiD-related practice specific to their jurisdiction. In this paper, we begin to examine the political ramifications and professional tensions arising from MAiD for nurses and nursing, focusing specifically upon the impacts for registered nurses. We identify how variations in the provincial and territorial literature and regulatory guidelines across Canada have given rise to role confusion and uncertainty among some registered nurses and how this may potentially impact patient care. We then continue to highlight the need for greater political activism among nurses to foster greater clarity in nursing roles in MAiD and to advocate for improved supports for patients and those close to them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it